Wo r k i n g Pa p e r S e r i e S
NO 1517 / f e b r u a r y 2013
Employment Duration and Shifts
into Retirement in the EU
Ted Aranki and Corrado Macchiarelli
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Acknowledgements
The authors are grateful to Julian Morgan, Bob Anderton and other participants at an internal seminar organized by the Directorate
Economic Developments of the European Central Bank. The authors also acknowledge comments from an anonymous referee. The
paper was drafted while Corrado was an economist at the ECB’s DG-Economics.
Ted Aranki
European Central Bank; e-mail: ted.aranki@ecb.europa.eu
Corrado Macchiarelli
London School of Economics and Political Science; e-mail: c.macchiarelli@lse.ac.uk
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ABSTRACT
The decision to cease working is traditionally influenced by a wide set of socio-economic and
environmental variables. In this paper, we study transitions out of work for 26 EU countries over the
period 2004-2009 in order to investigate the determinants of retirement based on the Eurostat Survey
on Income and Living Conditions (EU-SILC). Applying standard survivor analysis tools to describe
exits into retirement, we do not find any significant differences in the patterns into retirement between
the average euro area and EU non-euro area countries. Moreover, we find that shifts into retirement
have increased during the onset of the 2009 economic and financial crisis. Income, together with
flexible working arrangements, is found to be important as regards early retirement decisions,
compared to retiring beyond the legal retirement age. Finally, we show that institutional measures
(such as, state/health benefits, minimum retirement age) could not be sufficient alone if individuals
withdraw earlier from the labour market due to a weakening of their health. Especially, these latter
results are of importance for structural and macroeconomic policy, for instance, in increasing the
employment of both people and hours worked against the background of population ageing.
JEL Classification
J14, J26, C41
Keywords
Retirement, ageing population, hazard model, duration analysis, Cox
regressions, EU countries.
1
NON-TECHNICAL SUMMARY
Population ageing is expected to result in a slowdown of labour force growth and, later, into its
contraction and change in composition, as projected by the “2012 Ageing Report” by the European
Commission (2011). While it is accepted that the demographic shift will add to pressure on the
sustainability of public finances in many European countries, the implications for the long-term
growth of the labour force are still open issues. If, on the one hand, labour demand is expected to be
lower, owing to the shrinkage of the working age population, on the other hand, participation rates for
certain age cohorts could increase, given that working lives will be longer. Both aspects prompt
several policy questions on labour market developments; such as how to promote longer working lives
or how to improve choices for those workers forced to continue to work late in their lives.
Policy makers have been promoting the expansion of working lives, finding measures to postpone the
labour market activity. However, reflecting retirement patterns, the decision to enter retirement will no
longer be a discrete choice: with some workers remaining fully in employment and/or others reducing
the number of hours worked as they age (see also Hurd, 1993). Therefore, understanding in greater
details the motivations for retirement is key as it could assist the formulation of policies encouraging
the return of retirees to employment or decreasing the incentives of withdrawing earlier from the
labour market.
In this paper a wide set of socio-economic and environmental variables is used to study exits into
retirement in the EU. Based on longitudinal data from the Eurostat Survey on Income and Living
Conditions (EU-SILC), over the period 2004-2009, we analyse the probability of retiring at a given
age, given that the person has not retired yet.
The contribution of this paper can be gauged under two perspectives. First, we provide, for the first
time, results for a large set of 26 EU countries, by providing a systematic, conditional approach to
estimate labour market shifts into retirement. Secondly, we exploit cross country differences,
including measures of between-country heterogeneity, in quantifying the size and the speed with
which employment-to-retirement changes took place.
Since it would be natural to hypothesize upfront that retirement dynamics has changed over time –
especially during 2009, reflecting the extent to which the global economic and financial crisis hit in
most countries – and differs across euro area versus non-euro area countries – reflecting regionspecific dynamics and institutional set ups – we model this explicitly. Nonetheless, the results in this
paper do not support any significant differences in the patterns into retirement between the average
euro area and EU non-euro area countries. However, shifts into retirement seem to have increased
during the onset of the 2009 crisis, when controlling for income.
2
Turning to personal and household-level characteristics, income and benefits (also temporary in
nature, e.g., sickness benefits) are found to be important as regards early retirement decisions – when
accumulated income/wealth is presumably lower – compared to retiring beyond the legal retirement
age. In the same vein, flexible working arrangements are found to be important in order to keep people
at work beyond the legal retirement age, thus suggesting that making use of partial working schemes
could modify retirement patterns towards postponing the labour market withdrawal.
Finally, this analysis shows that institutional measures (such as, state/health benefits, minimum
retirement age) could not be sufficient alone if individuals withdraw earlier from the labour market
due to a weakening of their health. Particularly, for early retirees, policies aimed at improving the
health of the workforce and at keeping people who experience health problems active may be crucial.
Especially, these latter results have implications for the effectiveness of active labour market policies,
by getting retired people back into work or helping the prolongation of long term employment spells.
Moreover, these findings are of importance for structural and macroeconomic policy, for instance, in
increasing the employment of both people and hours worked against the background of population
ageing.
3
1
INTRODUCTION
Population ageing is expected to result in a slowdown of labour force growth and, later, into its
contraction and change in composition, as projected by the “2012 Ageing Report” by the European
Commission (2011). While it is accepted that the demographic shift will add to pressure on the
sustainability of public finances in many European countries, the implications for the long-term
growth of the labour force are still open issues. If, on the one hand, labour demand is expected to be
lower, owing to the shrinkage of the working age population, on the other hand, participation rates for
certain age cohorts could increase as well given that working lives will be longer, foreseeing pension
reforms.
Policy makers have been promoting the expansion of working lives finding measures to postpone the
labour market activity. However, understanding in greater details the motivations for retirements could
assist the formulation of policies that might encourage the return of retirees to employment or decrease
the incentives of withdrawing earlier from the labour market.
Workers are often assumed to dace the choice of leaving the labour market based on their own
preferences (Fengler, 1975; Hayward, Grady and McLaughlin, 1988) and/or based on the trade-off
between market work versus home production or leisure (for an overview see Duggan, 1984; Bazzoli
1985; Blöndal and Scarpetta 1999; Duval 2003; Gruber and Wise 2002; Meghir and Whitehouse
1997). This latter specification has been particularly supported in modern micro-founded models (e.g.,
of the New Keynesian type) for macroeconomic analysis. In practice, however, different constraints
can influence the labour force participation decisions of the elderly.
In this paper a wide set of socio-economic and environmental variables is employed to study exits into
retirement in the EU. Based on longitudinal data from the Eurostat Survey on Income and Living
Conditions (EU-SILC), covering the period 2004-2009, we analyse the probability of retiring at a
given age, given that the person has not retired yet. In particular, we study the transitions from
employment into (early) retirement by using a hazard based duration model framework (see also
Diamond and Hausman, 1984; Hagan, Jones and Rice, 2009; Jones, Rice and Roberts, 2010).
Our findings point to the existence of no significant differences, on average, in the patterns of
retirement between euro area and the EU non-euro area countries. Moreover, shifts into retirement
have increased during the onset of the 2009 economic and financial crisis, when controlling for
income effects.
Income and benefits (also temporary in nature, e.g., sickness benefits) are found to be important also
as regards early retirement decisions – when accumulated income/wealth is presumably lower –
compared to retiring beyond the legal retirement age. In the same vein, flexible working arrangements
are found to be important in order to keep people at work beyond the legal retirement age.
4
Finally, the analysis shows that, among all the examined institutional measures (such as, state/health
benefits, minimum retirement age, etc.), none could be sufficient alone if individuals withdraw earlier
from the labour market due to a weakening of their health. Particularly, for early retirees, policies
aimed at improving the health of the workforce and at keeping people who experience health problems
active may be crucial.
Particularly, these latter results have implications for the effectiveness of active labour market
policies, by getting retired people back into work or helping the prolongation of long term
employment spells. Moreover, the findings are of importance for structural and macroeconomic
policy, for example, in increasing the employment of both people and hours worked against the
background of population ageing.
The reminder of the paper is organized as follows: Section 2 describes the data and provides a brief
descriptive analysis. Section 3 presents the econometric strategy. Section 4 outlines the main results.
Section 5 concludes.
5
2
DATA
In this paper we use the Eurostat Survey on Income and Living Conditions (EU-SILC) which consist
of a database available in yearly frequencies, based on a rotating panel of longitudinal data for 4 subsamples. The EU-SILC provides the longest time series of comparable and consistently defined
individual level data for income and living conditions available for the EU, and our sample consists of
26 countries covering the period 2004-2009. The sample excludes Germany owing to data
unavailability. 1 Compared to other surveys, the EU-SILC provides not only details on each
individual’s personal characteristics (i.e. gender, age, marital status, education, family composition,
etc.), but also information on the level of (household) income prior to retiring and measures of the
individual’s wealth status. This represents an advantage compared to other analyses, given that income
and wealth can be important determinants of retirement decisions (see for instance, Hanoch and
Honig, 1983; Mitchel and Fields, 1984; Dugan, 1984; Ruhm, 1990). 2
An individual’s transition from employment into retirement is the event of interest in this study. From
the EU-SILC, we construct transitions from employment to retirement, or of remaining in
employment, based on each respondent’s current and past activity status.
Moving from employment to retirement, or retiring in the next period, is typically referred to as a
‘failure’ event which can occur at any point in time after an ‘onset of risk’ period is defined. Here, the
‘onset of risk’ period is defined as each individual’s first entry into the labour market.
To analyse transitions from employment into retirement, a hazard based duration model framework is
employed (see, Diamond and Hausman, 1984; Hagan, Jones and Rice, 2009; Jones, Rice and Roberts,
2010). This allows modelling the length of time spent at work before moving into retirement. The
dependent variable is thus the amount of time that an individual spends in employment before entering
retirement (i.e. employment duration). 3
One statistical motivation for employing a duration analysis framework includes the presence of
censored and left-truncated data. In practice, not all observations’ full history is observed until the
‘failure’ event. This naturally classifies the EU-SILC data as right-censored. Instead, the left
truncation problem refers to the fact that individuals become at risk or even fail before we can enrol
them in the study (see Figure 1).
1
Germany is covered by EU-SILC but their longitudinal microdata are not disseminated according to the EC Regulation no.
223/2009.
2
Overall, however, the EU-SILC is not designed to distinguish between job transitions and short retirement spells during the
working life. Yet, although there exists an observed retirement framework based around state pension eligibility in each
country (see OECD, 2011), people make transitions into and out of work until advanced ages, making observed employment
histories rather complex.
3
In Table A3 in the Appendix we detail the variables used in the estimation.
6
Figure 1: Examples of left truncation in the EU-SILC data
Left truncation is a natural feature of our data and involves the impossibility of tracking individuals
over the whole working life. Taking into account this particular data structure, an individual’s full
employment history is inferred based on retrospective information about the age at which he / she first
started to work and the years spent on paid work. This formalisation does not clearly take into account
the possibility of multiple ‘failure’ events within the same employment history, but rather assumes that
each individual’s working history is continuous until retirement.
In the same vein, individuals can enter the observation period / being enrolled in the study upon
having already retired. Here, an important difference compared to standard duration analyses is that
the failure event does not represent a rationale for an individual to drop out of sample (e.g., as death).
Whenever enrolment occurs conditional on a previous retirement event (see Figure 1(b)), there may
exist a positive difference between employment duration and the year of enrolment in the study,
representing a gap of information about each individual’s activity between the period he / she ceased
working and the period he / she became under observation. Only those with a gap < |G|, where G is
arbitrarily chosen, as well as those reporting to have most recently changed their activity status ‘from
employment to retirement’ are considered in this analysis. The gap variable is however not restricted
to be exactly zero, i.e. G = 3, allowing for reporting errors in (i) age, (ii) age of the first job and (iii)
7
the number of years spent on paid work. Importantly, this is not found to significantly affect our
results, as the inclusion of a whole set of covariates in the regressions will anyway require censoring
on many of these individuals with 0 < gap < |G|. 4
As individuals’ working histories are inferred based on retrospective questions, only the last spells are
considered, for individuals employed at all times. Conversely, only the spell of retirement is
considered for individuals retired in the sample. This allows data tractability in such a duration
analysis framework.
The dataset employed consists of 209183 individuals. Out of this number, 6756 individuals, that is just
over 3% of the sample, are observed retiring. As Table 1 suggests, the majority of these retirees ceased
working at the age of 55 or later. Women represent nearly 40% of the sample.
Table 1: Retirees by age group and gender
Age Groups
Percent of all observations
Males
Females
Total
Age 0 to 24
Age 25 to 29
Age 30 to 34
Age 35 to 39
Age 40 to 44
Age 45 to 49
Age 50 to 54
Age 55 to 59
Age 60 to 65
Age > 66
Total observations
0.1
0.2
0.2
0.4
1.3
2.6
5.2
25.0
47.9
17.2
4191
0.1
0.1
0.2
0.4
0.8
1.9
5.0
40.7
37.8
12.9
2565
0.1
0.2
0.2
0.4
1.1
2.3
5.1
31.0
44.1
15.6
6756
Gender representation is reversed for ages between 55 and 59, where women represent the majority of
the sample considered. In general, female workers retire earlier than males. Nearly 50% of female
workers enter into retirement before the age of 60, compared to 35% of the male workers.
In our sample, the age of retirement spans from a minimum of 20 years to a maximum of 80 years,
with the average occurring at the age of 60.4 and the median at 60. Thus, the distribution of retirees is
clearly skewed towards older people (see Table 2).
4
The analysis in the empirical section requires individuals to be observed at least for two consecutive periods (t-1 and t). For
instance, an individual retired at time t, should provide information on his previous (t-1) employment status (be it part time or
full time employment) or the occupation sector in which he / she most recently worked prior to retiring. Thus, when
individuals are enrolled upon having retired, information on previous employment status is clearly missing, making those
individuals not eligible for the empirical analysis in Section 4.
8
Table 2: Distribution of retirees by age
Percentiles
1%
5%
10%
25%
41
50
55
57
50%
60
75%
90%
95%
99%
64
67
70
78
Smallest
20
20
21
22
Largest
80
80
80
80
9
Obs
6756
Mean
Std. Dev.
60.4
6.3
Variance
Skewness
Kurtosis
40.3
-0.66
7.4
3
EMPIRICAL METHODOLOGY
In this paper we employ a hazard based duration model framework to study the transitions from
employment into retirement. The main advantage of using duration analysis is that it allows modelling
the length of time spent in a given state (i.e., employment) before moving into another state (i.e.
retirement). Relative to other approaches such as those that focus on the unconditional probability of
an event taking place (e.g., probit or logit models), our focus here is on the conditional probability, or,
the probability that the spell of one particular status (e.g., employment) will end in the next short
interval of time, given that it has lasted until recently.
As the analysis is concerned with the timing of the observed change from employment to retirement
(or ‘failure’ event, see Section 2), it makes sense to conceptualize the length of each individual j’s
employment spell as a random variable, Tj . 5 Assuming Tj has a continuous probability
distribution f (t ) , where t is a realisation of T j , the cumulative distribution function of T will be given
t
by F ( t ) = Pr( T j ≤ t ) = ∫ f ( s )ds . This says that the survival function for the j-th individual, or the
0
probability that his employment spell T is of length at least equal to t, is:
∞
S ( t ) = 1 − F ( t ) = Pr( T j > t ) = ∫ f ( s )ds
(1)
t
Conversely, the hazard rate (or instantaneous failure rate) for individual j at time t, is defined instead
as the marginal probability of immediate retirement, conditional on not having retired before time t:
h(t ) = P (t < T j < t + dt | T j > t ) =
f (t )
f (t )
=
1 − F (t ) S (t )
(2)
This class of models can be distinguished between non-parametric, semi-parametric and full
parametric models on the basis of whether they predict the probability distribution of a certain event
by means of a set of additional covariates. While parametric models are widely used across numerous
fields of economics, the Cox proportional hazard model (Cox, 1972; 1975) has proven particularly
flexible. Compared to fully parametric approaches, a key benefit of this approach is that it allows to
“avoid having to make assumptions about the nature of the duration times in the first place” (BoxSteffensmeier and Jones, 2004). In other words, the Cox model makes no assumption about the shape
of the hazard function or about how covariates may affect this shape. 6 Thus, the Cox semi-parametric
approach is regarded as a benchmark in this paper, whereas non-parametric (Section 4.1) and fully
5
See Box-Steffensmeier and Sokhey (2010) and Jenkins (2008) for a methodological overview.
6
For further references see Cleves et al. (2010).
10
parametric (Appendix) approaches are employed for preliminary investigation of the data
and
robustness checks respectively. 7
In the Cox model, the hazard for the j-th individual in the data is assumed to be:
h(t | x j ) = h0 (t )exp(β ′x j )
(3)
where β ′ is the vector of regression coefficients; x a vector of covariates which influence the hazard
rate; and h0 (t ) is the baseline hazard function. 8 By default, the model assumes a baseline hazard that
is common to all the individuals in the study population. In this way, covariates act multiplicatively on
the baseline hazard, adding additional risks on an individual basis, as determined by the individuals'
prognostic information. This gives the model a simple and easily understood interpretation. The main
idea behind it is the separation of the time effect in the baseline hazard function, on one side, and the
effect of the covariates in an exponential term, on the other. In essence this assumption says that the
hazard of failure at time t is related to individuals or groups of individuals by a proportionality
constant which does not depend on t.
3.1
FRAILTY MODELS
When observations are conditionally different in terms of their hazards due to unobserved
heterogeneity, standard models, as the one just described, may lead to spurious duration dependence. 9
Hence, fitting a normal duration model, e.g. equation (3), would simply not recognise that some
observations are more ‘frail’ (or, failure prone) than others.
A first possible solution would be to include fixed effects. However, it has been shown that fixed
effects are not a viable alternative in this context, as there is an incidental parameter problem that leads
to inconsistent and deflated standard errors (see Box-Steffensmeier and Zorn, 2001; Zorn, 2000). An
alternative method is to use random effects or ‘frailty’ models instead. The basic idea behind frailty
models is to introduce an additional random parameter into the hazard rate accounting exactly for
unobserved heterogeneity. Frailties may be individual-specific or group-specific. Models constructed
in terms of group-level frailties are typically referred to as ‘shared’ frailty models because
7
Fully parametric models will be efficient only as long as the distributional assumptions are appropriately chosen in the class
of parametric lifetime distributions (e.g., exponential, weibull, gompertz, log-normal, log-logistic or gamma). Clearly, if the
hazard function shape is incorrectly specified, parameters can be seriously biased.
8
In particular, when inference is dependent on the form of exp(β’x) but still independent of h0(t), one speaks of a semiparametric model (see Cox; 1972, 1975).
9
The notion of unobserved heterogeneity amounts here to observations being conditionally different in terms of their hazards
in ways that are unaccounted for in the systematic part of the model.
11
observations within a sub-group are assumed to share unmeasured risk factors prompting them to fail
earlier.
Lancaster (1979) proposed a parametric mixed proportional hazard model, accounting for unobserved
‘frailties’, which is a generalization of Cox’s (1972) approach. This specifies the hazard rate for the jth individual as (see also Box-Steffensmeier and Jones, 2004; Zorn, 2000): 10
h(t | x j ) = h0 (t )exp(β ′x j )exp(v j )
where v j = W jψ
(4)
describes the individual- or group-specific unobserved heterogeneity. For
identification purposes, the mean of v is typically normalized to unity and its variance is assumed to
equal an (unknown) parameter θ . 11 Compared to the standard Cox (1972) regression approach,
integrating v out leaves with the only problem of estimating the additional parameter, θ , in the
survivor function: 12
t
t
0
0
S (t | v) = ∫ − h( s, v)ds = −v ∫ h0 ( s )ds
(5)
10
In essence the concept goes back to the work of Greenwood and Yule (1920) on accident proneness. The term frailty itself
was introduced by Vaupel et al. (1979) in univariate survival models.
11
As Box-Steffensmeier and Jones (2004) note, we always make assumptions about whether we use frailty models or not.
When we do not take account of frailty, we are essentially assuming that v=1 with probability 1.
12
To derive the expected value of the survivor function, a probability distribution for v needs to be specified. Albeit the
gamma is the most common in the literature, any continuous distribution with positive support, a unit mean, and a finite
variance θ – inverse Gaussian, log-normal etc. would be appropriate. Essentially, as long as we assume that v has some
distribution, we can estimate the frailty model by estimating the frailty variance term θ.
12
4
RESULTS
4.1
NON-PARAMETRIC ANALYSIS
In order to summarise the data and visualise the distribution shape of employment duration for the
sample or for separate groups, non-parametric estimation of the survivor and hazard functions relying
on product-limit estimators are introduced. 13 Table 3 reports the survivor and cumulative hazard
function for employment duration. The survivor function shows the proportion of people who remain
in employment (i.e., do not ‘fail’ by entering retirement) as time proceeds, while the cumulative
hazard shows the expected number of ‘failures’ at each observed time. On average, after 40 years of
work, the survivor function starts decaying very rapidly, with the proportion of people still employed
decreasing over time. This is in line with the idea that the definable pensionable age requires around
40 years of contribution, consistent with the evidence in OECD (2011) and European Commission
(2011). Still, different conditions may apply depending on the number of years of contributions
achieved at a certain date or the age of first entry into the pension system.
As shown in Table 3, after 45 years of work the probability of remaining in employment is around
0.64, indicating that roughly 36% of the sampled individuals where retired. Furthermore, the NelsonAalen cumulative hazard suggests that the hazard of exiting into retirement increases monotonically.14
Survivor functions from employment to retirement across different categories, as well as by country,
are plotted in Figure A1 and A2 in the Appendix.
13
We use the Kaplan-Meier (1985) and the estimators dating back to Nelson (1972) and Aalen (1978) (referred to as NelsonAalen estimator) for the estimation of the survivor and cumulative hazard function respectively. For further details see also
Kiefer (1988).
14
It is worth noting that for the survivor function and the cumulative hazard function, both the Kaplan-Meyer and the
Nelson-Aalen estimators are consistent estimates of each function respectively, and their statistics are asymptotically
equivalent (see Klein and Moeschberger, 2003).
13
Table 3: Kaplan-Meier survival and Nelson-Aalen cumulative hazard functions
Years of work
Beginning total
Failures
Survivor function
Standard error
Cumulative hazard
Standard error
20
86945
19
0.9989
0.0001
0.0011
0.0001
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
37
20953
362
0.9408
0.0014
0.0608
0.0015
38
18164
422
0.9189
0.0017
0.0840
0.0019
39
15431
394
0.8954
0.0021
0.1095
0.0023
40
13184
623
0.8531
0.0026
0.1568
0.0030
41
10509
435
0.8178
0.0030
0.1982
0.0036
42
8705
508
0.7701
0.0035
0.2565
0.0044
43
7076
373
0.7295
0.0039
0.3093
0.0052
44
5820
325
0.6888
0.0043
0.3651
0.0060
45
4751
333
0.6405
0.0047
0.4352
0.0072
46
3723
238
0.5995
0.0051
0.4991
0.0083
47
3010
173
0.5651
0.0054
0.5566
0.0094
48
2459
145
0.5318
0.0058
0.6156
0.0106
49
1995
110
0.5024
0.0061
0.6707
0.0118
50
1672
228
0.4339
0.0067
0.8071
0.0149
51
1232
103
0.3976
0.0071
0.8907
0.0170
52
999
79
0.3662
0.0073
0.9697
0.0192
53
819
63
0.3380
0.0076
1.0467
0.0215
54
684
38
0.3193
0.0078
1.1022
0.0233
55
583
54
0.2897
0.0080
1.1948
0.0265
56
473
32
0.2701
0.0082
1.2625
0.0291
57
386
32
0.2477
0.0084
1.3454
0.0325
58
324
22
0.2309
0.0086
1.4133
0.0356
59
261
15
0.2176
0.0087
1.4708
0.0386
60
218
23
0.1946
0.0090
1.5763
0.0444
61
167
17
0.1748
0.0093
1.6781
0.0508
62
123
9
0.1620
0.0095
1.7512
0.0564
63
98
10
0.1455
0.0099
1.8533
0.0650
64
79
11
0.1252
0.0102
1.9925
0.0773
65
62
35
0.0545
0.0091
2.5570
0.1228
Note: The standard error for the Kaplan-Meyer estimate is the one given by Greenwood (1926).
14
4.2
SEMI-PARAMETRIC ANALYSIS
In this section, estimates of the semi-parametric Cox proportional hazard models are presented. As
discussed in Section 3, parametric analysis offers an advantage over the non-parametric methods, as it
allows predicting the probability distribution of retirement by means of a set of additional covariates.
In what follows the joint effect of various individual and labour market characteristics affecting the
probability of exiting into retirement is analysed.
In Table 4, the estimated results from the Cox’s proportional hazard model are reported. 15 The
reported coefficients are hazard ratios. 16 As explained in Section 3, a ‘shared’ frailty model is
employed where the sub-groups are selected according to the number of countries in our sample (26
countries). Thus, the model assumes that observations share group-specific, unmeasured, risk factors
that prompt exits into retirement. As the frailty terms explicitly account for the extra variance
associated with such risk factors, we can evaluate the hypothesis that θ = 0 to determine whether the
choice of treating unobserved heterogeneity in the model is motivated. Supporting our concerns, the
nested model under θ = 0 is always preferred to the reference non-frailty model according to the
relevant LR test at the bottom of Table 4.
Focusing on the regression results, the estimated hazard ratios indicate that there is no significant
difference in the patterns of retirement between residents in the euro area (EA) and EU non-euro area
countries. Moreover, the results suggest that the onset of the global financial crisis (2009) significantly
increased flows into retirement. However, it is only when controlling for household disposable income
and personal benefits that we achieve this result. When omitting the income variables from the
regression, the result rather suggest that the hazard to retire decreased in 2009. 17 The importance of the
income variables for the result of the 2009 crisis on the hazard to retire may stem from the fact that
wealth for people eligible to retirement generally became at risk in 2009, with income to cover basic
expenses in retirement running short because of the financial crisis. 18
15
A sensitivity analysis is performed in the Appendix showing that the results from the Cox proportion model are robust also
when using full parametric models.
16
A coefficient of, e.g., 0.5 for a dummy variable is interpreted as lowering the exit rate from employment to retirement by a
half. For a continuous variable, a coefficient of 0.5 implies that a one unit change in the variable is associated with a hazard
rate of 1/2 as large and an n unit change in the variable is associated with a hazard rate (1/2)n as large.
17
The finding of a decreasing hazard to retire in 2009 is consistent with the non-parametric Kaplan-Meier estimates reported
in Figure A1 (Appendix). However, it should be borne in mind that the Kaplan-Meier estimates are not conditional on any
covariates. Thus, the information extracted from a plain visual inspection of the plots in Figure A1 is very limited, compared
to the semi-parametric analysis.
18
In the present context it is however difficult to distinguish labour market quits from lay-offs.
15
Table 4: Cox regressions
Hazard
ratio
(Std. Err.)
Disposable income
0.982
(0.025)
Old age benefits
2.028***
(0.085)
Unemployment benefits
1.864***
(0.117)
Variable
Hazard ratio
(Std. Err.)
Variable
EA
1.030
(0.320)
Income variables
Dummy 2009
1.301***
(0.049)
Minimum retirement age
0.896***
(0.012)
Individual characteristics
Female
1.479***
(0.075)
Disability benefits
2.806***
(0.230)
Married
1.024
(0.082)
Sickness benefits
1.134
(0.088)
Skilled
1.676***
(0.073)
Interaction
Part-time
0.127***
(0.061)
Part-time x disp. income
1.171***
(0.056)
Occupational group
Health variables
2.Occ. group
0.656***
(0.033)
Health
1.682***
(0.201)
3.Occ. group
0.958
(0.048)
Health(-1)
1.460**
(0.224)
4.Occ. group
0.924
(0.055)
Partner characteristics
5.Occ. group
0.853**
(0.053)
2.Partner unemployed
1.384***
(0.134)
3.Partner retired
1.065*
(0.041)
4.Partner inactive
1.155***
(0.055)
Partner's health
0.706***
(0.083)
Statistics
θ
0.577
LR test (frailty terms)
Prob>=chi-bar-sq. = 0.000
Wald χ2
1305.892
Prob > χ2
0.000
Log-likelihood
-32839.825
Number of groups
26
Observations
53490
(0.150)
Note: Standard errors in parentheses. *** denotes p<0.01, ** p<0.05, * p<0.1. See Table A3 for data and definitions.
From Table 4 we also find that setting a minimum retirement age reduces the hazard to retire. This can
be interpreted in the light of providing workers with a yardstick or a minimum number of years with
payment to social security before they become eligible to retire.
Beyond more institutional factors, the participation of elderly workers is also affected by a wide set of
socio-economic and environmental variables such as gender (female) and occupation groups (occ.
group). While we find increased movements toward retirement among female workers, we do not find
any statistical difference as to whether a person is married or not.
16
Besides, the personal characteristics typically associated with higher education (skilled) are not found
to generally lead workers to work longer than their less-educated counterparts. This is somewhat
consistent with the findings in Autor and Dorn (2009) reporting an inverted U-shape relationship
between skills and changes in the mean age, suggesting that occupations in the bottom and top deciles
of the skill distribution tend to work on average less than people with middle-skill jobs.
Working (or having worked) part-time plays an important role in reducing the hazard to retire. This is
consistent with the recent evidence (see, inter alia, Machado and Portela, 2012) that retirement is no
longer likely to be a discrete choice: with some workers exiting from full-time employment and
making use of flexible working schemes before withdrawing completely from the labour market.
Furthermore, some occupation groups are found to have important explanatory power. Compared to
the category of professionals, technicians and associate professionals (occ. group =1), those belonging
to the category including service, skilled agricultural and fishery workers (occ. group =2), and those
with elementary occupations (occ. group =5), show a significantly lower probability to retire. Albeit
with such sectoral categories
it is not possible to distinguish between private or public sector
employees (see Table A3, in the Appendix), these results probably reconcile with the idea that formal
workers are expected to retire earlier than casual workers and self-employed, typically belonging to
some of the categories listed above (i.e., elementary occupations, agriculture and fishing).
Looking at the income variables, household disposable income ultimately does not exert an influence
on retirement decisions in our sample. Nevertheless, the interaction between working part-time and
disposable income (part-time x disp. income) significantly reduces employment duration. This result
suggests that there exist a level effect of income when employment is not full-time, or, the exposure to
the current (or past) level of income is higher when not working full-time (see also Blake, 2007;
Montalto, 2001).
Income supports are very likely to influence the labour supply of the aged as well, given that
unemployment schemes may induce older workers to seek part-time jobs or to withdraw earlier from
the labour market. 19 The variation in age of eligibility for social security benefits (old age, disability
and/or sickness benefits) can particularly affect the sustainability of the retirement status. It should be
borne in mind that the effect of pension schemes and benefits are not exogenous to income, as pension
scheme produce inter-temporal substitution effects (i.e. with a postponement of the retirement age
today in favour of an expected higher pension return tomorrow). In this setting, receiving positive old
age benefits or unemployment benefits significantly increases retirement decisions, in line with the
19
On the other hand, as argued by Boskin and Hurd (1978), if higher social security taxes are needed to finance the
increasing burden of an ageing population, this could create disincentives for people to reduce their labour force participation
and withdraw earlier from the labour market.
17
idea that social insurance schemes such as disability benefits significantly increase flows out of
employment (see estimated hazard ratios in Table 4). Consistently, sickness benefits, representing cash
benefits that replace (in whole or in part) loss of earnings during temporary inability to work, are not
found to significantly affect the hazard to retire. 20
In line with the literature, our findings also point to the fact that health is an important determinant of
retirement, as healthier people are found to continue to work and retire later (see inter alia, Bound,
1991; Jones et al., 2008; 2010; Deschryvere, 2005; Disney et al., 2006). 21 Overall, however – as
highlighted by a growing literature (e.g., Jones et al. 2008; 2010) – measures of health are subject to
an endogeneity problem. There are several reasons on why to expect an endogeneity bias when using
self-reporting measures of health. First, self-reported health is based on subjective assessments which
may not be comparable across individuals (Lindeboom, 2006; Lindeboom and van Doorslaer, 2004).
Second, there is an obvious simultaneity problem between self-reported health and the labour market
status, given that health problems may represent a legitimate reason for a person in the working age to
be outside the labour market (Kerkhofs and Lindeboom, 1995; Kreider, 1999). Finally, for some
individuals there may be incentives to report health problems as a mean to obtain disability benefits
(i.e., the so-called ‘disability’ route into retirement, see Blundell et al., 2002).
Many studies in the literature typically use an instrumental variable approach, by adopting more
‘objective’ measures of health to instrument self-reported health measures. Along these lines, an
‘individual health stock’ is normally constructed, where self-reported health is regressed on a set of
specific health problems (see also, Griliches, 1974; Fuller, 1987). As such questions concerning
specific health problems are not available in the EU-SILC, we take into account the possibility that
anticipated retirement may justify the reporting of bad health by, first, including a dummy whenever
individuals receives disability benefits. This allow us to control for possible ‘disability routes’ into
retirement (Blundell et al., 2002). Further, to assess the robustness of our previous findings, alternative
health measures are employed, along with the usual set of covariates. More specifically, in Table 5
measures arguably less prone to reporting bias than self-reported health are employed, such as a
measure of health limitations (limit) and chronic diseases (see Jones et al., 2008; 2010).
20
Note that, in the EU-SILC, unemployment benefits also include (see also Table A3):
(i) Partial unemployment benefits compensating for the loss of wages or salary due to formal short-time working
arrangements, and/or intermittent work schedules, irrespective of their cause, and where the employer/employee relationship
continues.
(ii) Early retirement for labour market reasons, including periodic payments to older workers who retire before reaching
standard retirement age due to unemployment or to job reductions caused by economic measures such as the restructuring of
an industrial sector or of a business enterprise. These payments normally cease when the beneficiary becomes entitled to an
old age pension.
Thus, receiving unemployment benefits may unveil information about part-time working schemes and early retirement
patterns in some cases.
21
For a survey of the literature see Deschrivere (2005).
18
Using alternative health measures has generally a size effect on the coefficients of interest while it
does not affect their sign and / or significance. Thus, independently of the proxy employed, health
status is an important determinant of retirement decisions.
Table 5: Cox regressions using alternative health measures
Hazard ratio
(Std. Err.)
Hazard ratio
(Std. Err.)
Health
1.682***
(0.201)
Health(-1)
1.460**
(0.224)
Limit
1.210***
(0.076)
Limit(-1)
1.213***
(0.083)
Chronic
Chronic(-1)
Wald χ2
Prob > χ
2
Log-likelihood
Number of groups
Observations
Hazard ratio
(Std. Err.)
1.050
(0.041)
0.919**
(0.036)
1305.892
1304.008
1282.473
0.000
0.000
0.000
-32839.825
-32842.068
-32851.073
26
26
26
53490
53490
53490
Note: All regressions include a full set of covariates as in Table 4. The whole results are available upon request from the
authors. Standard errors in parentheses. *** denotes p<0.01, ** p<0.05, * p<0.1. See Table A3 for data and definitions.
To assess whether a change in labour market status from employment to retirement is more influenced
by a (negative) shock to an individual health or a level effect via slow health deterioration, a ‘health
shock’, or a lagged health variable, is included in the regression, following the discussion in Jones et
al. (2010). It seems plausible that the health lag is more informative about the decision to retire than
current health as it normally takes time to entirely adjust to health limitations and to allow an
individual to gauge his reduced ability to work over time. The use of health lag has the great
advantage of reducing any endogeneity bias by observing the timing before the decision to effectively
retire (see Jones et al., 2010). In Table 4, the effect of the health shocks is significant. This is broadly
consistent with the evidence obtained when using alternative health shock measures (see Table 5). 22
Occupation statuses and health effects are important also as regard to individuals’ partners. For
instance, predictions regarding a joint labour market decision of old couples can derive from a family
labour supply model like the one proposed by Killingsworth and Heckman (1986) were couples
22
Health is also important as concern the interaction with occupation groups (occ. group). Such an interaction
term suggests that those who work in craft and related trades workers (including heavy works such as extraction
and building) have higher incentives to retire due to (reported) health problems. For sake of brevity these results
are not reported in Table 4, but are available upon request from the authors.
19
maximise a single utility function subject to a household budget constraint with pooled income. The
analysis in this paper confirms the prediction that having a partner retired significantly increases the
hazard to retire, compared to having a partner employed. This is in line with the idea that the primary
reason for partners to retire together is shared preferences / substitution effect for leisure against
working longer, with each partner valuing more retirement when the partner is retired as well (see
Killingsworth and Heckman, 1986; Hurd, 1990; Michaud, 2003). Moreover, individuals with partners
reporting bad health are generally associated with a lower probability to retire compared to individuals
with partners reporting better health status (see also Wu, 2003).
4.2.1 EARLY-RETIREMENT DECISIONS
Until now the analysis has focused on individuals retiring. However, understanding the motivations to
retire earlier (before the legal retirement age), compared to standard retirement patterns, represent an
important factor of analysis. This can be important, especially in the light of assisting the formulation
of policies that might encourage early retirees to stay at work.
Cox estimates of early retirement decisions are reported in Table 6. Although the results are similar to
those presented for the full sample, significant differences do exist. In particular:
•
Working (or having worked) with a part-time contract does not play a significant role in reducing
the hazard to retire early. This finding, combined with the result in Table 4, may suggests that a
gradual reduction in hours worked over the last segment of the working life can contribute to
increased employment of older workers, beyond the legal retirement age.
•
Higher disposable household income and state / health benefits – including those temporary in
nature, such as sickness benefits – significantly increase the hazard to retire early, compared to
standard retirement decisions. This suggests that the choice of pre-retiring should be considered in
the light of the expected retirement needs, or the evaluation of whether the accumulated income /
wealth prior to retiring is considered adequate to sustain the future retirement status. In this vein,
early retirements are more sensible to income effects (including short term benefits) compared to
retiring after the legal retirement age. Along the same lines, and opposite to the results in Table 4,
the interaction term between part time and income does not exert any significant effect on early
retirement decisions.
•
Finally, focusing on the individual health status, we show that – analogously to the findings in
Table 4 – people with health problems are generally found to discontinue employment and retire
earlier. However, the health coefficient for people retiring below the legal retirement age is twice
as big the one reported in Table 4. This points to the fact that, among all the institutional measures
20
scrutinized (long term and short term benefits, minimum retirement age, etc.), none could be
sufficient alone if individuals withdraw earlier from the labour market due to a weakening of their
health. Particularly for early retirees, policies aimed at advance retirement by improving the health
of the workforce and at keeping those who experience health problems active may be essential.
Table 6 Cox regressions, early retirement sample
Variable
Hazard ratio
(Std. Err.)
Variable
EA
0.854
(0.273)
Income variables
Dummy 2009
1.285***
(0.061)
Minimum retirement age
0.848***
(0.015)
Individual characteristics
Hazard ratio
(Std. Err.)
Disposable income
1.082**
(0.036)
Old age benefits
3.330***
(0.156)
Unemployment benefits
1.637***
(0.112)
Female
1.373***
(0.084)
Disability benefits
2.291***
(0.202)
Married
1.135
(0.112)
Sickness benefits
1.318***
(0.121)
Skilled
1.914***
(0.102)
Interaction
Part-time
1.201
(0.751)
Part-time x disp. income
0.962
(0.059)
Occupational group
Health variables
2.Occ. group
0.746***
(0.045)
Health
2.383***
(0.338)
3.Occ. group
0.832***
(0.049)
Health(-1)
1.765***
(0.325)
4.Occ. group
0.796***
(0.054)
Partner characteristics
5.Occ. group
0.784***
(0.058)
2.Partner unemployed
1.379***
(0.146)
3.Partner retired
1.110**
(0.052)
4.Partner inactive
1.101*
(0.060)
Partner's health
0.682**
(0.109)
Statistics
θ
0.604
LR test (frailty terms)
Prob>=chi-bar-sq. = 0.000
Wald χ2
1475.982
Prob > χ2
0.000
Log-likelihood
-22076.371
Number of groups
26
Observations
51304
(0.157)
Note: Standard errors in parentheses. *** denotes p<0.01, ** p<0.05, * p<0.1. See Table A3 for data and definitions.
4.3
FRAILTY TERMS
To control for the fact that some countries may be more prone to retirement than others for unobserved
reasons not captured by our covariates, a ‘shared’ frailty model has been used. The terms for the 26
21
EU member states from our ‘shared’ frailty model are shown in Figure 2. Particularly, the panel on the
left-hand side of the figure show the estimated frailty terms from the regression in Table 4. The righthand side panel of Figure 2 shows results the results from the regression in Table 6. Cases above the 0
line are the most failure-prone ones.
The results, in Figure 2, provide a mixed picture with some large euro area countries lying below the
zero line (i.e. Italy, Spain, the Netherlands) while others, e.g. France and Belgium, lying slightly above
zero. These results confirm our previous findings, suggesting that the hazard to retirement is mixed
and can not be reconciled with membership to the euro area. For the early retirement regression, the
picture changes only slightly with some countries moving around the zero line (e.g. France, Italy and
Denmark).
Although, there are no significant differences across regions there are clear differences across
countries. On average, however, more prone to retirement countries are also those who are more prone
to retire earlier.
Figure 2 Frailty terms for EU member states, retirement (left-hand side) and early retirement (righthand side)
22
5
CONCLUSIONS
Schemes to curb public expenditures by increasing the minimum retirement age represents important
arguments of discussion in the bargaining set up (see also Hicks, 2011). However, understanding in
greater details the motivations for retirements could assist the formulation of policies that might
encourage the return of retirees to employment or decrease the incentives of withdrawing earlier from
the labour market.
Workers are often assumed to dace the choice of leaving the labour market based on their own
preferences (Fengler, 1975; Hayward, Grady and McLaughlin, 1988) and / or based on the trade-off
between market work versus home production or leisure (for an overview see Bazzoli, 1985; Blöndal
and Scarpetta, 1999; Duval, 2003; Gruber and Wise, 2002; Meghir and Whitehouse, 1997). In
practice, however, different constraints can influence the labour force participation decision of the
elderly.
In this paper a wide set of socio-economic and environmental variables is employed to study exits into
retirement in the EU. Based on longitudinal data from the Eurostat Survey on Income and Living
Conditions (EU-SILC), over the period 2004-2009, we analyse the probability of retiring at a given
age, given that the person has not retired yet. A number of stylized facts are documented.
First, after controlling for (un)measured risk factor affecting the hazard to retire in each country, we
find no significant differences, on average, in the patterns of retirement between residents in the euro
area and the EU non-euro area countries. Second, shifts into retirement have increased during the
onset of the 2009 economic and financial crisis, when controlling for income effects. Income and
benefits are found to be important also as regards early retirement decisions, when accumulated
income / wealth is presumably lower, compared to retiring beyond the legal retirement age. In the
same vein, flexible working arrangements are found to be particularly important for workers to keep
working beyond the legal retirement age. Thus, making use of partial working arrangements could
modify retirement patterns towards postponing the age of withdrawing from the labour market.
Finally, this analysis shows overall that, among all the institutional measures scrutinized (state/health
benefits, minimum retirement age, etc.), none could be sufficient alone if individuals withdraw from
the labour market before the legal retirement age due to a weakening of their health. Particularly, for
early retirees, policies aimed at improving the health of the workforce and at keeping people who
experience health problems active may be essential.
All in all, by jointly testing for a wide set of factors affecting retirement decisions in the EU, the
results of this paper illustrate that adequate policies to retain old workers at work can only be
appropriately formulated once the determinants of retirement decisions are fully understood and
modelled. In our knowledge, this paper represents a first attempt in this direction for the whole EU.
23
REFERENCES
Aalen, O.O. (1978). Nonparametric inference for a family of counting processes, Annals of Statistics,
6 701-726 in Cleves M., Gutierrez R. G., Gould W., Marchenko Y.V. (2010), An Introduction to
Survival Analysis using Stata, Stata Press, Texas, US.
Akaike, H. (1974). A new look at the statistical model identification, IEEE Transaction on Automatic
Control, 19:716-723.
Autor, D., and Dorn, D. (2009). This Job Is "Getting Old": Measuring Changes in Job Opportunities
Using Occupational Age Structure, American Economic Review, American Economic Association,
vol. 99(2), 45-51.
Bazzoli, G.J. (1985). The Early Retirement Decision: New Empirical Evidence the Influence of
Health. The Journal of Human Resources, Vol. 20, No. 2. (Spring), pp. 214-234.
Blake, D. (2004). The impact of wealth on consumption and retirement behaviour in the UK, Applied
Financial Economics, 14(8), 555-576.
Blöndal, S., and Scarpetta, S. (1999). The Retirement Decision in OECD Countries, OECD Economics
Department Working Papers, No. 202, OECD Publishing.
Bound, J. (1991), Self-Reported Versus Objective Measures of Health in Retirement Models, Journal
of Human Resources, University of Wisconsin Press, vol. 26(1), pages 106-138.
Boskin, M.J., and Hurd, M.D. (1978). The effect of social security on early retirement, Journal of
Public Economics, Elsevier, vol. 10(3), pages 361-377.
Box-Steffensmeier, J., and Jones, B. (2004). Timing and Political Change: Event History Modeling in
Political Science. Ann Arbor: University of Michigan Press.
Box-Steffensmeier, J., and Zorn, C. (2001). Duration Models and Proportional Hazards in Political
Science, American Journal of Political Science 45:972–988.
Box-Steffensmeier, J., and Sokhey, A.E. (2010). Event History Methods in K.T. Leicht and J.C.
Jenkins (eds.), Handbook of Politics: State and Society in Global Perspective, Handbooks of
Sociology and Social Research.
Cleves, M., Gutierrez, R.G., Gould, W., and Marchenko, Y.V. (2010). An Introduction to Survival
Analysis using Stata, Stata Press, Texas, US.
Cox, D.R. (1972). Regression Models and Life Tables (with Discussion), Journal of the Royal
Statistical Society, Series B 34:187—220 in Cleves, M., Gutierrez, R.G., Gould, W., and Marchenko
Y.V. (2010), An Introduction to Survival Analysis using Stata, Stata Press, Texas, US.
Cox, D.R. (1975). Partial likelihood, Biometrika 62, 269–76.
Deschryvere, M. (2005). Health and Retirement Decisions: An Update of the Literature. ENEPRI
Research Report. No 6.
Diamond, P., and Hausman, J. (1984). Individual Retirement and Saving Behavior, Journal of Public
Economics, 23: 81-114.
24
Disney, R., Emmerson, C., and Wakefield, M. (2006). Ill health and retirement in Britain: A panel
data-based analysis, Journal of Health Economics, vol. 25(4), pages 621-649, July.
Duggan, J.E. (1984). The Labour-Force Participation of Older Workers, Industrial and Labour
Relations Review, 37(3), 416-430.
Duval, R. (2003). The Retirement Effects of Old-Age Pension and Early Retirement Schemes in
OECD Countries. EOCD Economics Department Working Paper No. 370. Paris: OECD.
European Commission (2011). The 2012 Ageing Report: Underlying Assumptions and Projection
Methodologies, European Economy 4.
Fengler, A.P. (1975). Attitudinal orientation of wives towards their husbands' retirement. International
Journal of Aging and Human Development, 6: 139- 152.
Fields, G.S., and Mitchell, O.S. (1984). The effects of social security reforms on retirement ages and
retirement incomes, Journal of Public Economics, Elsevier, vol. 25(1-2), pages 143-159.
Gehan, E.A. (1965). A generalized Wilcoxon test for comparing arbitrarily singly-censored samples,
Biometrika, 52: 203-223 in Cleves, M., Gutierrez, R.G., Gould, W., and Marchenko, Y.V. (2010), An
Introduction to Survival Analysis using Stata, Stata Press, Texas, US.
Grambsch, P.M., and Therneau, T.M. (1994). Proportional hazard tests and diagnostics based on
weighted residuals, Biometrika, 81: 515-526 in Cleves, M., Gutierrez, R.G., Gould, W., and
Marchenko, Y.V. (2010), An Introduction to Survival Analysis using Stata, Stata Press, Texas, US.
Greenwood, M., and Yule, G.U. (1920). An Inquiry into the Nature of Frequency Distributions of
Multiple Happenings, with Particular Reference to the Occurrence of Multiple Attacks of Disease or
Repeated Accident, Journal of the Royal Statistical Society A, Vol. 83, pp. 255-279.
Greenwood, M. (1926). The natural duration of cancer, Reports on Public Health and Medical
Subjects, 33, 1–26 in Cleves, M., Gutierrez, R.G., Gould, W., and Marchenko, Y.V. (2010), An
Introduction to Survival Analysis using Stata, Stata Press, Texas, US.
Gruber, J., and Wise, D.A. (2002). Social Security Programs and Retirement around the World: Micro
Estimation, NBER Working Paper No. 9407.
Hagan, R., Jones, A.M., and Rice, N. (2009). Health and Retirement in Europe. International Journal
of Environmental Research and Public Health, 6(10), pages 2676-2695.
Hanoch, G., and Honig, M. (1983). Retirement, Wages, and Labor Supply of the Elderly, Journal of
Labor Economics, 1, 131-51.
Hayward, M.D., Grady, W.R., and McLaughlin, S.D. (1988). The retirement process among older
women in the United States. Research on Aging, 10: 358-382.
Hicks, P. (2011). The surprisingly large policy implications of changing retirement durations, SEDAP
Research Paper No. 284.
Hurd, M.D. (1990) , Research on the Elderly: Economic Status, Retirement, and Consumption and
Saving, Journal of Economic Literature, American Economic Association, vol. 28(2), pages 565-637.
Hurd, M.D. (1993). The Effect of Labor Market Rigidities on the Labor Force Behavior of Older
Workers, NBER Working Papers 4462.
25
Jenkins, S.P. (2008). “Survival Analysis.” Unpublished manuscript, Institute for Social and Economic
Research, University of Essex, Colchester.
Jones, A., Rice N., and Roberts, J. (2008). Early retirement and inequality in Britain and Germany:
How important is health? Working Papers 2008012, The University of Sheffield, Department of
Economics.
Jones, A., Rice, N., and Roberts, J. (2010). Sick of work or too sick to work? Evidence on selfreported health shocks and early retirement from the BHPS, Economic Modelling 27 866-880.
Kaplan, E.L., and Meier, P. (1958). Nonparametric estimation from incomplete observations, Journal
of the American Statistical Association, 53, 457-481 in Cleves M., Gutierrez R. G., Gould W.,
Marchenko Y. V. (2010), An Introduction to Survival Analysis using Stata, Stata Press, Texas, US.
Kerkhofs, M., and Lindeboom, M. (1995). Subjective health measures and state dependent reporting
errors. Health Economics, 4: 221–235.
Kiefer, N.M. (1988). Economic Duration Data and Hazard Functions, Journal of Economic Literature,
Vol. 26, No. 2, 646–679.
Killingsworth, M., and Heckman J. (1986). Female Labor Supply: A Survey, in O. Ashenfelter:
Handbook of Labor Economics. Amsterdam: North-Holland, 103-204.
Klein, J.P., and Moeschberger, M.L. (2003). Survival Analysis: Techniques for Censored and
Truncated Data, New York: Springer-Verlag in Cleves, M., Gutierrez, R.G., Gould, W., and
Marchenko, Y.V. (2010), An Introduction to Survival Analysis using Stata, Stata Press, Texas, US.
Kreider, B. (1999). Latent Work Disability and Reporting Bias, Journal of Human Resources,
University of Wisconsin Press, vol. 34(4), 734-769.
Lancaster, T. (1979). Econometric methods for the duration of unemployment, Econometrica, 47: 939956.
Lindeboom, M. (2006). Health and work of older workers in A. Jones, editor, The Elgar Companion to
Health Economics, pages 26–35. Edward Elgar Publishing.
Lindeboom, M., and van Doorslaer, E. (2004). Cut-point shift and index shift in self-reported health,
Journal of Health Economics, vol. 23(6), 1083-1099.
Machado, C.S., and Portela, M. (2012). Hours of Work and Retirement Behavior, IZA Discussion
Papers 6270.
Mantel, N., and Haenszel, W. (1959). Statistical aspects of the analysis of data from the retrospective
analysis of disease, Journal of the National Cancer Institute, 22: 719-748.
Meghir, C., and Whitehouse, E. (1997). Labour-market transitions and retirement of men in the UK,
Journal of Econometrics, vol. 79, pp. 327-354.
Michaud, P. (2003). Joint Labor Supply Dynamics of Older Couples". IZA Discussion Paper 832.
Montalto, C.P. (2001). Retirement Wealth and Its Adequacy: Assessing the Impact of Changes in the
Age of Eligibility for Full Social Security Benefits, Center for Retirment Research at Boston College,
CRR WP 20001-07.
26
Nelson, W. (1972). Theory and applications of hazard plotting for censored failure data,
Technometrics, 14, 945-965 in Cleves M., Gutierrez R. G., Gould W., Marchenko Y. V. (2010), An
Introduction to Survival Analysis using Stata, Stata Press, Texas, US.
OECD (2011). Pensions at a Glance 2011: Retirement-income Systems in OECD and G20 Countries,
OECD Publishing, http://dx.doi.org/10.1787/pension_glance-2011-en.
Ruhm, C. (1990). Determinants of the timing of retirement. In P. B. Doeringer (Ed.) Bridges to
retirement: Older workers in a changing labor market. (pp. 23-32). Ithaca, NY: ILR Press.
Schoenfeld, D. (1981). The asymptotic properites of nonparametric tests for comparing survival
distributions, Biometrika, 68: 316-319.
Vaupel, J.W., Manton, K.G., and Stallard, E. (1979). The Impact of Heterogeneity in Individual
Frailty on the Dynamics of Mortality, Demography 16, 439 – 454.
Wu, S. (2003). The effect of health events on the economic status of married couples, Journal of
Human Resource 38, 209–230.
Zorn, Christopher J.W. (2000). “Modeling Duration Dependence.” Political Analysis 8:367–380.
27
APPENDIX
Figure A1: Survival function for the transition from employment into retirement for separate
groups
Proportion not retired
0.25
0.50
0.75
0.00
0.00
EU10
5
Skills
Partner employed
Partner retired
Partner unemployed
Partner inactive
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
Male
Female
Old age benefits
Proportion not retired
0.75
0.25
0.50
0.00
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
0.00
5
5
Onset of the crisis
Proportion not retired
0.25
0.50
0.75
Proportion not retired
0.25
0.50
0.75
0.00
0
0
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
Pre-crisis
EA
Partner activity
1.00
0
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
1.00
5
1.00
0.00
0
Gender
1.00
1.00
2009
Proportion not retired
0.25
0.50
0.75
Proportion not retired
0.25
0.50
0.75
1.00
Regions
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
Medium- or Low-skilled
High-skilled
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
No benefits
Benefits
Note: Test results, i.e. log-rank (Mantel and Haenszel, 1959) and Wilcoxon (Breslow, 1970; Gehan, 1965), for the equality of
the different survivor functions suggest that the equality of the survivor functions is rejected.
28
Figure A2: Survival function for the transition from employment into retirement by country
DK
1.00
EU26
1.00
RO
5
EU26
EU26
1.00
UK
0.00
0.00
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
EU26
SK
1.00
1.00
Proportion not retired
0.25
0.50
0.75
1.00
SE
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
EU26
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
EU26
RO
Proportion not retired
0.25
0.50
0.75
Proportion not retired
0.25
0.50
0.75
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
UK
29
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
LU
PL
0
5
NL
0.00
5
PT
SK
1.00
0
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
5
EU26
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
EU26
Proportion not retired
0.25
0.50
0.75
0.00
0
5
MT
0.00
Proportion not retired
0.25
0.50
0.75
0
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
0
0.00
0.00
5
LV
PT
NL
1.00
EU26
1.00
0
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
HU
LU
LT
Proportion not retired
0.25
0.50
0.75
5
EU26
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
EU26
Proportion not retired
0.25
0.50
0.75
0.00
0
5
IT
MT
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
Proportion not retired
0.25
0.50
0.75
EU26
0.00
Proportion not retired
0.25
0.50
0.75
0
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
5
0.00
0.00
5
1.00
LV
1.00
0
IE
0
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
EU26
PL
SI
1.00
EU26
LT
1.00
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
ES
HU
GR
Proportion not retired
0.25
0.50
0.75
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
EU26
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
EU26
0.00
0
5
FR
IT
5
1.00
EU26
Proportion not retired
0.25
0.50
0.75
Proportion not retired
0.25
0.50
0.75
0.00
0
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
0
0.00
0.00
5
FI
IE
GR
1.00
EU26
1.00
0
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
CY
ES
EE
Proportion not retired
0.25
0.50
0.75
5
EU26
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
EU26
0.00
0
5
DK
FR
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
Proportion not retired
0.25
0.50
0.75
EU26
Proportion not retired
0.25
0.50
0.75
Proportion not retired
0.25
0.50
0.75
0.00
0
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
5
0.00
0.00
5
1.00
FI
1.00
0
CZ
0
BG
Proportion not retired
0.25
0.50
0.75
EU26
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
EE
1.00
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
5
EU26
Proportion not retired
0.25
0.50
0.75
5
0.00
0
BE
Proportion not retired
0.75
0.25
0.50
EU26
1.00
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
0.00
Proportion not retired
0.25
0.50
0.75
0.00
0
Proportion not retired
0.75
0.50
0.25
1.00
Proportion not retired
0.25
0.50
0.75
5
1.00
CZ
1.00
0
AT
Proportion not retired
0.25
0.50
0.75
EU26
CY
0.00
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
1.00
5
Proportion not retired
0.25
0.50
0.75
0
BG
0.00
Proportion not retired
0.25
0.50
0.75
1.00
BE
0.00
0.00
Proportion not retired
0.25
0.50
0.75
1.00
AT
SE
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Years of work
EU26
SI
Sensitivity analysis of the model: full-parametric regressions
In order to investigate if the estimated Cox coefficients are robust, in what follows we present results
from a full-parametric model. 23 As discussed in Section 3.1, when applying parametric models it is
necessary to specify a certain functional form of the hazard rate that fits the data. The likelihood-ratio
or Wald test can be used to discriminate between groups of nested models (Cleves et al., 2010). In the
present case, the results of the likelihood-ratio test indicate that the generalized gamma distribution fits
well. 24 However, when models are not nested, likelihood-ratio or Wald test tests are not appropriate
and an alternative statistic has to be used. The most common is the Akaike Information Criterion
(AIC). Akaike (1974) proposed a method penalising each models’ log-likelihood to reflect the number
of parameters being estimated and then comparing them. 25
In Table A1 an overview of the computed AIC scores is presented. There are slight differences in the
value of the log-likelihood function between the models. Although the log-logistic distribution scores
best, the results reveal that the Weibull model is the preferred specification in the proportional hazard
form. 26 Note, also that the Weibull model has virtually the same AIC scores as the log-logistic one. 27
Furthermore, as shown in the non-parametric analysis above, the hazard of exiting into retirement
exhibits a monotonically increase. Thus, based on these combined assessment, the more reasonable
Weibull distribution is employed. 28 Estimates using the log-logistic distribution do not produce any
relevant differences compared to the Weibull estimates, suggesting that the selection effect of using
distributions other than the Weibull is limited.
23
One of the assumptions underlying the Cox model is the proportional hazards assumption. Evaluating the robustness of the
estimated Cox proportional hazard models, it is shown (from the results in Section 4.2) that the joint Wald test of all
coefficients equal to 0 is rejected at a standard significance level in all cases. However, the test of the proportional hazards
assumption using Schoenfeld’s (1982) residuals is rejected. Since we are more interested in the parameter estimates than the
shape of the hazard in this paper, the Cox proportional hazard model is, nevertheless, well-suited to this goal.
24
We start from a generalized gamma model for evaluating and selecting an appropriate parametric model. We test the
hypothesis that the ancillary parameters for the generalized gamma distribution (with standard deviation) kappa = 0 (model is
log-normal); kappa = 1 (model is Weibull); and kappa = 1 and sigma = 1 (model is exponential). By testing the appropriate
restrictions, it is found that we can reject the log-normal, the Weibull and the exponential distribution against the gamma for
all samples.
25
The AIC compares the likelihood scores while taking into account the degrees of freedom used in each model. AIC = -2*
log-likelihood + 2 * (k + c), where k is the number of model covariates and c the number of model-specific distributional
parameters.
26
Since the Weibull can be specified in both the proportional hazard and accelerated failure time form we can compare it to
other accelerated failure time distributional forms.
27
This is the case also for the generalized gamma distribution.
The Weibull model assumes a baseline hazard of the form h0(t) = ptp-1 exp(β0), where p is some ancillary shape parameter
estimated from the data, and the scale parameter is parameterised as exp(β0). The Weibull distribution can provide a variety
of monotonically increasing or decreasing shapes of the hazard function, and their shape is determined by the estimated
parameter p.
28
30
Table A1: Model selection for full parametric regressions, whole
sample (retirement)
Distribution (metric)
Log likelihood
k
c
AIC
Ranking
Exponential (PH, AFT)
-10001
23
1
20049
6
Weibull (PH, AFT)
-2275
23
2
4599
3
Gompertz (PH)
-2544
23
2
5138
4
Log-normal (AFT)
-3445
23
2
6941
5
Log-logistic (AFT)
-2226
23
2
4503
1
Generalized gamma (AFT)
-2255
23
3
4563
2
Exponential (PH, AFT)
-10001
23
1
20049
6
Note: The models are estimated assuming gamma distributed frailty or heterogeneity.
In Table A2 the time ratios from the estimated – Weibull distributed – accelerated failure time model
are presented. The results of the Weibull model are basically consistent with those of the Cox
proportional hazard regression in Table 4. An important difference to bear in mind when interpreting
the results is that in proportional hazard models (such as Cox’s) the estimates are interpreted as the
effect on the employment exit rate; while accelerated failure time models analyse the effect on the
employment period. 29
29
Additionally, it should be noted from Table A2 that the Wald test on the ancillary shape parameter (p) indicates that we can
reject the hypothesis that the hazard is a constant, suggesting a monotone increasing behaviour over time. The hypothesis that
ln(p)=0 is rejected at the 1% significance level for all observations. The parameter p is the ‘shape’ parameter, as it defines the
shape of the distribution. If p= 1, then the hazard is constant. For other values of p, the Weibull hazard is not constant; it is
monotone decreasing when p < 1 and monotone increasing when p > 1.
31
Table A2: Full parametric regressions
Variable
Time ratio
(Std. Err.)
Variable
EA
Dummy 2009
Minimum retirement age
Individual characteristics.
Female
Married
Skilled
Part-time
Occupational group
2.Occ. group
3.Occ. group
4.Occ. group
5.Occ. group
1.001
0.971***
1.012***
(0.037)
(0.004)
(0.002)
0.955***
0.996
0.944***
1.319***
(0.006)
(0.009)
(0.005)
(0.075)
1.057***
1.002
1.006
1.014*
(0.006)
(0.006)
(0.007)
(0.007)
Income variables
Disposable income
Old age benefits
Unemployment benefits
Disability benefits
Sickness benefits
Interaction
Part-time x disp. income
Health variables
Health
Health(-1)
Partner characteristics
2.Partner unemployed
3.Partner retired
4.Partner inactive
Partner's health
Constant
Statistics
ln(p)
ln(θ)
LR test (frailty terms)
Wald χ2
Prob > χ2
Log-likelihood
Number of groups
Observations
Hazard ratio
(Std. Err.)
1.001
0.925***
0.926***
0.880***
0.988
(0.003)
(0.004)
(0.007)
(0.008)
(0.009)
0.979***
(0.006)
0.947***
0.961**
(0.013)
(0.017)
0.964***
0.990**
0.979***
1.047***
23.550***
(0.011)
(0.004)
(0.005)
(0.014)
(2.559)
8.521***
(0.084)
0.589**
(0.153)
Prob>=chi-bar-sq. = 0.000
1215.992
0.000
-1407.851
26
53490
Note: Standard errors in parentheses. *** denotes p<0.01, ** p<0.05, * p<0.1. See Table A3 for data and definitions
Table A3: Variables used in the estimation
Variable
Definition
Dependant variables
Employment The amount of time that an individual spends in employment before entering into
duration retirement.
Employment The amount of time that an individual spends in employment before entering into
duration, early early retirement (before the legal retirement age).
Explanatory variables
Main activity status during the income reference period. If the main activity is not ‘a
job or business’, the status is self-defined. The main activity status during the income
Activity
reference period is ‘at work’ if the respondent worked (or was in paid apprenticeship
or training) the majority of weeks during the income reference period. If a person
spends the same number of weeks in different activities, priority should be given to
32
Employed
Unemployed
Retired
Inactive
Change activity
Employed - retired
Unemployed retired
Retired - employed
Retired unemployed
Retired - inactive
Inactive - retired
EA
Dummy 2009
Minimum ret. age
Female
Married
Skilled
Occ. Groups
1
2
3
4
5
Part-time
Disposable
income
Part-time X disp.
income
economic activity (‘main activity job or business’) over non-economic activity and
over inactivity.
Equals 1 if the individual is at work. A person is at work if he works at least 1 hour
during the reference week.
Equals 2 if the individual is unemployed
Equals 3 if the individual is in retirement or early retirement
Equals 4 if the individual classifies himself as any other inactive person.
Most recent change in the individual’s activity status. The variable records changes in
the individual activity status over the last interview (or last 12 months for the first
year of data collection).
Equals 1 if the individual changed from employment to retirement.
Equals 2 if the individual changed from unemployment to retirement.
Equals 3 if the individual changed from retirement to employment.
Equals 4 if the individual changed from retirement to unemployment.
Equals 5 if the individual changed from retirement to inactive other than retirement.
Equals 6 if the individual changed from inactivity other than retirement to
retirement.
Equals 1 if a country belongs to the euro area.
Equals 1 if year of the survey equals 2009.
Countries' minimum retirement age according to OECD (2011).
Equals1 if the interviewed is of female gender.
Equals 1 if the interviewed is married.
Equals1 if the interviewed has high education according to the highest ISCED level
attained. This includes first stage of tertiary education (not leading directly to an
advanced research qualification) and second stage of tertiary education (leading to
an advanced research qualification.
The variable conforms to the ISCO-88 (COM) International Standard Classification of
Occupations.
Equals 1 if the individual belongs to legislators, senior officials and managers,
professionals, technicians and associate professionals or clerks.
Equals 2 if the individual belongs to service workers and shop and market sales
workers, skilled agricultural and fishery workers.
Equals 3 if the individual belongs to craft and related trades workers.
Equals 4 if the individual belongs to plant and machine operators and assemblers.
Equals 5 if the individual has a elementary occupation.
Equals 1 if the individual works or worked part-time based on a self-defined
economic status.
(Log) total disposable household income. This includes the sum for all household
members of gross personal income components (gross employee cash or near cash
income; gross non-cash employee income; company car; employers’ social insurance
contributions; gross cash benefits or losses from self-employment (including
royalties); value of goods produced for own consumption; pensions received from
individual private plans; unemployment benefits; old-age benefits; survivor' benefits,
sickness benefits; disability benefits and education-related allowances plus gross
income components at household level (imputed rent; income from rental of a
property or land; family/children related allowances; social exclusion not elsewhere
classified; housing allowances; regular inter-household cash transfers received;
interests, dividends, profit from capital investments in unincorporated business;
income received by people aged under 16) minus (employer’s social insurance
contributions interest paid on mortgage; regular taxes on wealth; regular interhousehold cash transfer paid; tax on income and social insurance contributions).
Interaction term between part-time and disposable income.
33
Equals 1 if the individual receives non-zero old age benefits. By definition, the old age
function refers to the provision of social protection against the risk linked to old age,
loss of income, inadequate income, lack of independence in carrying out daily tasks,
Old age benefits
reduced participation in social life, and so on. Old age benefits cover benefits that:
provide a replacement income when the aged person retires from the labour market,
or guarantee a certain income when a person has reached a prescribed age.
Equals 1 if the individual receives non-zero disability benefits. Disability benefits
refer to benefits that provide an income to persons below standard retirement age
whose ability to work and earn is impaired beyond a minimum level laid down by
Disability benefits legislation by a physical or mental disability. Disability is the full or partial inability to
engage in economic activity or to lead a normal life due to a physical or mental
impairment that is likely to be either permanent or to persist beyond a minimum
prescribed period.
Equals 1 if the individual receives non-zero sickness benefits. Sickness benefits refer
to cash benefits that replace in whole or in part loss of earnings during temporary
Sickness benefits
inability to work due to sickness or injury. Being temporary in nature, those include
only paid leave or cash benefits in case of self-reported sickness or injury or that of a
dependent child.
Equals 1 if the individual receives non-zero unemployment benefits. Unemployment
benefits refer to benefits that replace in whole or in part income lost by a worker due
to the loss of gainful employment; provide a subsistence (or better) income to
persons entering or re-entering the labour market; compensate for the loss of
Unempl. benefits
earnings due to partial unemployment; replace in whole or in part income lost by an
older worker who retires from gainful employment before the legal retirement age
because of job reductions for economic reasons; contribute to the cost of training or
re-training people looking for employment; or help unemployed persons meet the
cost of travelling or relocating to obtain employment.
Equals 1 if the individual assesses his health is 'very bad'. The measurement of selfperceived health is, by its very nature, subjective. The notion is restricted to an
assessment coming from the individual and not from anyone outside that individual.
The reference is to health in general rather than the present state of health, as the
Health
question is not intended to measure temporary health problems. It is expected to
include the different dimensions of health, i.e. physical, social and emotional function
and biomedical signs and symptoms. It omits any reference to an age as respondents
are not specifically asked to compare their health with others of the same age or with
their own previous or future health state.
Main activity status of the partner (if any) during the income reference period. See
Partner's activity
Activity definition
Employed Equals 1 if the partner is at work.
Unemployed Equals 2 if the partner is unemployed
Retired Equals 3 if the partner is in retirement or early retirement.
Inactive Equals 4 if the partner is inactive.
Equals 1 if each individual's partner (if any) assesses his health to be 'very bad'. See
Partner's health
Health definition.
Alternative health measures
Equals 1 if the individual reports limitations in activities because of health problems.
The purpose of the instrument is to measure the presence of long-standing
limitations, as the consequences of these limitations (e.g. care, dependency) are more
Limit
serious. The period of at least the last 6 months is relating to the duration of the
activity limitation and not of the health problem. The answer to this question is yes
(1 or 2) if the person is currently limited and has been limited in activities for at least
the last 6 months.
Equals 1 if the individual reports to suffer from any a chronic (long-standing) illness
Chronic
or condition.
Note: See also the EU-SILC’s Guidelines.
34